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Recent research in neuroimaging has focused on assessing associations between genetic variants that are measured on a genomewide scale and brain imaging phenotypes. A large number of works in the area apply massively univariate analyses on a genomewide basis to find single nucleotide polymorphisms that influence brain structure. In this paper, we propose using various dimensionality reduction methods on both brain structural MRI scans and genomic data, motivated by the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We also consider a new multiple testing adjustment method and compare it with two existing false discovery rate (FDR) adjustment methods. The simulation results suggest an increase in power for the proposed method. The real-data analysis suggests that the proposed procedure is able to find associations between genetic variants and brain volume differences that offer potentially new biological insights.

Original publication

DOI

10.1093/biostatistics/kxu026

Type

Journal article

Journal

Biostatistics

Publication Date

01/2015

Volume

16

Pages

17 - 30

Keywords

Distance covariance, Genomewide association studies, Local false discovery rate, Multivariate analysis, Neuroimaging analysis, Positive false discovery rate, Alzheimer Disease, Brain, Cognitive Dysfunction, Data Interpretation, Statistical, Genetic Variation, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Neuroimaging, Phenotype